Modeling scientists are increasingly turning to Phenotype MicroArrays as one tool to validate and optimize in silico models using wet lab data. Phenotype MicroArray tests cover most of core metabolism allowing many comparisons to be made to metabolic models. Discrepancies between models and PM data facilitate model expansion and iterative optimization. In this issue, we present three papers on this topic.
Genome Scale Metabolic Reconstruction and Constraints-Based Modeling of the Antarctic Bacterium Pseudoalteromonas haloplanktis TAC125
The authors created a metabolic model of a cold-adapted bacterium from Antarctic sea water. The model was used to investigate metabolic adjustments at low temperature. After model refinement using PM data, the agreement rate was found to be 84%. Through integration of various omics datasets, the affects of temperature on the model were studied.
Global insights into Energetic and Metabolic Networks in Rhodobacter sphaeroides
Purple non-sulfur bacteria have extensive metabolic and energetic diversity. Phenotype MicroArrays were used to expand the genome-scale metabolic reconstruction of this bacterium. Important differences in PM carbon substrate utilization were found between aerobic respiratory and photosynthetic conditions increasing understanding of the photosynthetic lifestyle.
Reconstruction and Analysis of a Genome-scale Metabolic Model for Scheffersomyces stipitis
Scheffersomyces stipitis has the highest known native capacity for xylose utilization. A metabolic model was created for this xylose fermenting hemiascomycetous yeast. The model could predict qualitative outcomes of Phenotype MicroArray substrate utilization in 74% of cases and serves as basis for metabolic network analysis and design.
Phenotype MicroArray Technology
Biolog’s Phentoype MicroArray technology enables researchers to evaluate nearly 2000 phenotypes of a microbial cell in a single experiment. This integrated system of cellular assays, instrumentation and bioinformatics software provides cellular knowledge that complements molecular information, helping you interpret and find the relevant aspects in massive amounts of gene expression or proteomics data. Through comprehensive and precise quantitation of phenotypes, researchers are able to obtain an unbiased perspective of the effect on cells of genetic differences, environmental change, exposure to chemicals or drugs, and more.